U.S. patent number 11,455,563 [Application Number 16/578,142] was granted by the patent office on 2022-09-27 for noise reduced circuits for trapped-ion quantum computers.
This patent grant is currently assigned to IONQ, INC.. The grantee listed for this patent is IONQ, INC.. Invention is credited to Isaac Hyun Kim, Omar Shehab.
United States Patent |
11,455,563 |
Shehab , et al. |
September 27, 2022 |
Noise reduced circuits for trapped-ion quantum computers
Abstract
Embodiments described herein are generally related to a method
and a system for performing a computation using a hybrid
quantum-classical computing system, and, more specifically, to
providing an approximate solution to an optimization problem using
a hybrid quantum-classical computing system that includes a group
of trapped ions. A hybrid quantum-classical computing system that
is able to provide a solution to a combinatorial optimization
problem may include a classical computer, a system controller, and
a quantum processor. The methods and systems described herein
include an efficient and noise resilient method for constructing
trial states in the quantum processor in solving a problem in a
hybrid quantum-classical computing system, which provides
improvement over the conventional method for computation in a
hybrid quantum-classical computing system.
Inventors: |
Shehab; Omar (Hyattsville,
MD), Kim; Isaac Hyun (Menlo Park, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
IONQ, INC. |
College Park |
MD |
US |
|
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Assignee: |
IONQ, INC. (College Park,
MD)
|
Family
ID: |
1000006584316 |
Appl.
No.: |
16/578,142 |
Filed: |
September 20, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20200372390 A1 |
Nov 26, 2020 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62852264 |
May 23, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06E
3/005 (20130101); G06N 10/00 (20190101) |
Current International
Class: |
G06N
10/00 (20220101); G06E 3/00 (20060101) |
Foreign Patent Documents
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WO-2019220122 |
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Nov 2019 |
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WO |
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Other References
Wang, Y., Um, M., Zhang, J., An, S., Lyu, M., Zhang, J. N., Duan,
L. M., Yum, D., Kim, K. (2017). Single-qubit quantum memory
exceeding ten-minute coherence time. Nature Photonics, 11(10),
646-650. https://doi.org/10.1038/s41566-017-0007-1. cited by
applicant .
Webb, A. E., Webster, S. C., Collingbourne, S., Bretaud, D.,
Lawrence, A. M., Weidt, S., Mintert, F., & Hensinger, W. K.
(2018). Resilient entanglement gates for trapped ions. Physical
Review Letters, 121(18), 180501.
https://doi.org/10.1103/PhysRevLett.121.180501. cited by applicant
.
Wineland, D. J., Monroe, C., Itano, W. M., Leibfried, D., King, B.
E., & Meekhof, D. M. (1998). Experimental Issues in Coherent
Quantum-State Manipulation of Trapped Atomic Ions. Journal of
Research of the National Institute of Standards and Technology,
103(3), 259. https://doi.org/10.6028/jres.103.019. cited by
applicant .
Wright, K., Beck, K. M., Debnath, S., Amini, J. M., Nam, Y.,
Grzesiak, N., Chen, J. S., Pisenti, N. C., Chmielewski, M.,
Collins, C., Hudek, K. M., Mizrahi, J., Wong-Campos, J. D., Allen,
S., Apisdorf, J., Solomon, P., Williams, M., Ducore, A. M., Blinov,
A., . . . Kim, J. (2019). Benchmarking an 11-qubit quantum
computer. Nature Communications, 10, 5464.
https://doi.org/10.1038/s41467-019-13534-2. cited by applicant
.
Wu, Y., Wang, S. T., & Duan, L.-M. (2018). Noise analysis for
high-fidelity quantum entangling gates in an anharmonic linear Paul
trap. Physical Review A, 97(6), 062325.
https://doi.org/10.1103/PhysRevA.97.062325. cited by applicant
.
Zhu, S.-L., Monroe, C., & Duan, L.-M. (2006). Trapped ion
quantum computation with transverse phonon modes. Physical Review
Letters, 97(5), 050505.
https://doi.org/10.1103/PhysRevLett.97.050505. cited by applicant
.
Farhang Haddadfarshi et al. "High Fidelity Quantum Gates of Trapped
Ions in the Presence of Motional Heating", New Journal of Physics,
vol. 18, No. 12, Dec. 2, 2016, p. 123007, XP055722925. cited by
applicant .
International Search Report dated Sep. 4, 2020 for Application No.
PCT/US2020/034008. cited by applicant .
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung,
Xiao-Qi Zhou, Peter J Love, Alan Aspuru-Guzik, and Jeremy L
O'brien. A variational eigenvalue solver on a photonic quantum
processor. Nature communications, 5:4213, 2014. cited by applicant
.
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alan
Aspuru-Guzik. The theory of variational hybrid quantum-classical
algorithms. New Journal of Physics, 18(2):023023, 2016. cited by
applicant .
John Preskill. Quantum computing in the nisq era and beyond. arXiv
preprint arXiv:1801.00862, 2018. cited by applicant .
A Yu Kitaev. Quantum measurements and the abelian stabilizer
problem. arXiv preprint quantph/ 9511026, 1995. cited by applicant
.
Miroslav Dob{hacek over (s)}icek, Goran Johansson, Vitaly Shumeiko,
and GoranWendin. Arbitrary accuracy iterative quantum phase
estimation algorithm using a single ancillary qubit: A two-qubit
benchmark. Physical Review A, 76(3):030306, 2007. cited by
applicant .
PJJ O'Malley, Ryan Babbush, ID Kivlichan, Jonathan Romero, JR
McClean, Rami Barends, Julian Kelly, Pedram Roushan, Andrew
Tranter, Nan Ding, et al. Scalable quantum simulation of molecular
energies. Physical Review X, 6(3):031007, 2016. cited by applicant
.
Yangchao Shen, Xiang Zhang, Shuaining Zhang, Jing-Ning Zhang,
Man-Hong Yung, and Kihwan Kim. Quantum implementation of the
unitary coupled cluster for simulating molecular electronic
structure. Physical Review A, 95(2):020501, 2017. cited by
applicant .
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita,
Markus Brink, Jerry M Chow, and Jay M Gambetta. Hardware-efficient
variational quantum eigensolver for small molecules and quantum
magnets. Nature, 549(7671):242, 2017. cited by applicant .
JI Colless, VV Ramasesh, D Dahlen, MS Blok, ME Kimchi-Schwartz, JR
McClean, J Carter, WA De Jong, and I Siddiqi. Computation of
molecular spectra on a quantum processor with an errorresilient
algorithm. Physical Review X, 8(1):011021, 2018. cited by applicant
.
Raffaele Santagati, Jianwei Wang, Antonio A Gentile, Stefano
Paesani, Nathan Wiebe, Jarrod R Mc-Clean, Sam Morley-Short, Peter J
Shadbolt, Damien Bonneau, Joshua W Silverstone, et al. Witnessing
eigenstates for quantum simulation of hamiltonian spectra. Science
advances, 4(1):eaap9646, 2018. cited by applicant .
Cornelius Hempel, Christine Maier, Jonathan Romero, Jarrod McClean,
Thomas Monz, Heng Shen, Petar Jurcevic, Ben Lanyon, Peter Love,
Ryan Babbush, et al. Quantum chemistry calculations on a
trapped-ion quantum simulator. arXiv preprint arXiv:1803.10238,
2018. cited by applicant .
EF Dumitrescu, AJ McCaskey, G Hagen, GR Jansen, TD Morris, T
Papenbrock, RC Pooser, DJ Dean, and P Lougovski. Cloud quantum
computing of an atomic nucleus. arXiv preprint arXiv:1801.03897,
2018. cited by applicant .
N Klco, EF Dumitrescu, AJ McCaskey, TD Morris, RC Pooser, M Sanz, E
Solano, P Lougovski, and MJ Savage. Quantum-classical dynamical
calculations of the schwinger model using quantum computers. arXiv
preprint arXiv:1803.03326, 2018. cited by applicant .
Yunseong Nam, Jwo-Sy Chen, Neal C Pisenti, Kenneth Wright, Conor
Delaney, Dmitri Maslov, Kenneth R Brown, Stewart Allen, Jason M
Amini, Joel Apisdorf, et al. Ground-state energy estimation of the
water molecule on a trapped on quantum computer. arXiv preprint
arXiv:1902.10171, 2019. cited by applicant .
JKL MacDonald. On the modified ritz variation method. Physical
Review, 46(9):828, 1934. cited by applicant .
DH Weinstein. Modified ritz method. Proceedings of the National
Academy of Sciences, 20(9):529-532, 1934. cited by applicant .
Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon Benjamin, and
Xiao Yuan. Quantum computational chemistry. arXiv preprint
arXiv:1808.10402, 2018. cited by applicant .
Panagiotis KI Barkoutsos, Jerome F Gonthier, Igor Sokolov, Nikolaj
Moll, Gian Salis, Andreas Fuhrer, Marc Ganzhorn, Daniel J Egger,
Matthias Troyer, Antonio Mezzacapo, et al. Quantum algorithms for
electronic structure calculations: particle/hole hamiltonian and
optimized wavefunction expansions. arXiv preprint arXiv:1805.04340,
2018. cited by applicant .
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush,
and Hartmut Neven. Barren plateaus in quantum neural network
training landscapes. arXiv preprint arXiv:1803.11173, 2018. cited
by applicant .
O. Shehab, K. Landsman, Y. Nam, D. Zhu, N. M. Linke, M. Keesan, E.
F. Dumitrescu, A. J. McCaskey, G.Hagen, G.R. Jansen, T.D. Morris,
T. Papenbrock, R. C. Pooser, D. J. Dean, P. Lougovski, and C.
Monroe. Toward convergence of effective field theory simulations on
digital quantum computers. in preparation. cited by applicant .
Nikolaj Moll, Andreas Fuhrer, Peter Staar, and Ivano Tavernelli.
Optimizing qubit resources for quantum chemistry simulations in
second quantization on a quantum computer. Journal of Physics A:
Mathematical and Theoretical, 49 (29):295301, 2016. cited by
applicant .
Sergey Bravyi, Jay M Gambetta, Antonio Mezzacapo, and Kristan
Temme. Tapering off qubits to simulate fermionic hamiltonians.
arXiv preprint arXiv:1701.08213, 2017. cited by applicant .
Stuart Hadfield and Anargyros Papageorgiou. Divide and conquer
approach to quantum hamiltonian simulation. New Journal of Physics,
20(4):043003, 2018. cited by applicant .
Jin-Guo Liu, Yi-Hong Zhang, Yuan Wan, and Lei Wang. Variational
quantum eigensolver with fewer qubits, 2019. cited by applicant
.
Tyler Takeshita, Nicholas C Rubin, Zhang Jiang, Eunseok Lee, Ryan
Babbush, and Jarrod R McClean. Increasing the representation
accuracy of quantum simulations of chemistry without extra quantum
resources. arXiv preprint arXiv:1902.10679, 2019. cited by
applicant .
Isaac H Kim and Brian Swingle. Robust entanglement renormalization
on a noisy quantum computer. arXiv preprint arXiv:1711.07500, 2017.
cited by applicant .
Isaac H Kim. Noise-resilient preparation of quantum many-body
ground states. arXiv preprint arXiv:1703.00032, 2017. cited by
applicant .
Glen Evenbly and Guifre Vidal. Algorithms for entanglement
renormalization. Physical Review B, 79(14):144108, 2009. cited by
applicant .
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford
Stein. Introduction to algorithms. MIT press, 2009. cited by
applicant .
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum
approximate optimization algorithm. arXiv preprint arXiv:1411.4028,
2014. cited by applicant .
Suguru Endo, Simon C Benjamin, and Ying Li. Practical quantum error
mitigation for near-future applications. Physical Review X,
8(3):031027, 2018. cited by applicant .
Sam McArdle, Xiao Yuan, and Simon Benjamin. Error mitigated quantum
computational chemistry. arXiv:1807.02467, 2018. cited by applicant
.
Lewis F Richardson, BA J Arthur Gaunt, et al. Viii. the deferred
approach to the limit. Phil. Trans. R. Soc. Lond. A,
226(636-646):299-361, 1927. cited by applicant .
Debajyoti Bera, Stephen Fenner, Frederic Green, and Steve Homer.
Universal quantum circuits. arXiv preprint arXiv:0804.2429, 2008.
cited by applicant .
Shantanu Debnath. A programmable five qubit quantum computer using
trapped atomic ions. PhD thesis, 2016. cited by applicant .
Jerry Moy Chow. Quantum information processing with superconducting
qubits. Yale University, 2010. cited by applicant .
Klaus Molmer and Anders Sorensen. Multiparticle entanglement of hot
trapped ions. Physical Review Letters, 82(9):1835, 1999. cited by
applicant .
Kevin A Landsman, Caroline Figgatt, Thomas Schuster, Norbert M
Linke, Beni Yoshida, Norm Y Yao, and Christopher Monroe. Verified
quantum information scrambling. arXiv:1806.02807, 2018. cited by
applicant .
Shantanu Debnath, Norbert M Linke, Caroline Figgatt, Kevin A
Landsman, KevinWright, and Christopher Monroe. Demonstration of a
small programmable quantum computer with atomic qubits. Nature,
536(7614):63, 2016. cited by applicant .
Shi-Liang Zhu, C Monroe, and L-M Duan. Arbitrary-speed quantum
gates within large ion crystals through minimum control of laser
beams. EPL (Europhysics Letters), 73(4):485, 2006. cited by
applicant .
Dmitri Maslov. Basic circuit compilation techniques for an ion-trap
quantum machine. New Journal of Physics, 19(2):023035, 2017. cited
by applicant .
Dmitri Maslov and Yunseong Nam. Use of global interactions in
efficient quantum circuit constructions. New Journal of Physics,
20(3):033018, 2018. cited by applicant .
Ivan Kassal, Stephen P Jordan, Peter J Love, Masoud Mohseni, and
Alan Aspuru-Guzik. Polynomialtime quantum algorithm for the
simulation of chemical dynamics. Proceedings of the National
Academy of Sciences, pp. pnas-0808245105, 2008. cited by applicant
.
Nicholas J Ward, Ivan Kassal, and Alan Aspuru-Guzik. Preparation of
many-body states for quantum simulation. The Journal of chemical
physics, 130(19):194105, 2009. cited by applicant .
Jonathan Welch, Daniel Greenbaum, Sarah Mostame, and Alan
Aspuru-Guzik. Efficient quantum circuits for diagonal unitaries
without ancillas. New Journal of Physics, 16(3):033040, 2014. cited
by applicant .
Dawei Lu, Nanyang Xu, Ruixue Xu, Hongwei Chen, Jiangbin Gong,
Xinhua Peng, and Jiangfeng Du. Simulation of chemical isomerization
reaction dynamics on a nmr quantum simulator. Physical review
letters, 107(2):020501, 2011. cited by applicant .
Borzu Toloui and Peter J Love. Quantum algorithms for quantum
chemistry based on the sparsity of the ci-matrix. arXiv preprint
arXiv:1312.2579, 2013. cited by applicant .
Alan Aspuru-Guzik, Anthony D Dutoi, Peter J Love, and Martin
Head-Gordon. Simulated quantum computation of molecular energies.
Science, 309(5741):1704-1707, 2005. cited by applicant .
N Cody Jones, James D Whitfield, Peter L McMahon, Man-Hong Yung,
Rodney Van Meter, Alan Aspuru-Guzik, and Yoshihisa Yamamoto. Faster
quantum chemistry simulation on fault-tolerant quantum computers.
New Journal of Physics, 14(11):115023, 2012. cited by applicant
.
James D Whitfield, Jacob Biamonte, and Alan Aspuru-Guzik.
Simulation of electronic structure hamiltonians using quantum
computers. Molecular Physics, 109(5):735-750, 2011. cited by
applicant .
Dave Wecker, Bela Bauer, Bryan K Clark, Matthew B Hastings, and
Matthias Troyer. Gate-count estimates for performing quantum
chemistry on small quantum computers. Physical Review A,
90(2):022305, 2014. cited by applicant .
Pascual Jordan and Eugene Paul Wigner. uber das paulische
aquivalenzverbot. In the Collected Works of Eugene Paul Wigner, pp.
109-129. Springer, 1993. cited by applicant .
Jacob T Seeley, Martin J Richard, and Peter J Love. The
bravyi-kitaev transformation for quantum computation of electronic
structure. The Journal of chemical physics, 137(22):224109, 2012.
cited by applicant .
Sergey B Bravyi and Alexei Yu Kitaev. Fermionic quantum
computation. Annals of Physics, 298(1):210-226, 2002. cited by
applicant .
Andrew Tranter, Sarah Sofia, Jake Seeley, Michael Kaicher, Jarrod
McClean, Ryan Babbush, Peter V Coveney, Florian Mintert, Frank
Wilhelm, and Peter J Love. The b ravyi-k itaev transformation:
Properties and applications. International Journal of Quantum
Chemistry, 115(19):1431-1441, 2015. cited by applicant .
Oscar Higgott, Daochen Wang, and Stephen Brierley. Variational
quantum computation of excited states. arXiv preprint
arXiv:1805.08138, 2018. cited by applicant .
Suguru Endo, Tyson Jones, Sam McArdle, Xiao Yuan, and Simon
Benjamin. Discovering hamiltonian spectra with variational quantum
imaginary time simulation. arXiv preprint arXiv:1806.05707, 2018.
cited by applicant .
Jarrod R McClean, Mollie E Kimchi-Schwartz, Jonathan Carter, and
Wibe A de Jong. Hybrid quantumclassical hierarchy for mitigation of
decoherence and determination of excited states. Physical Review A,
95(4):042308, 2017. cited by applicant .
International Search Report dated May 28, 2020 for Application No.
PCT/US2020/015235. cited by applicant .
International Search Report dated May 28, 2020 for Application No.
PCT/US2020/015234. cited by applicant .
Aloul, F. A., Ramani, A., Markov, I. L., Sakallah, K. A. (2002).
Solving Difficult SAT Instances in the Presence of Symmetry.
Proceedings of the 39th Annual Design Automation Conference,
731-736. https://doi.org/10.1145/513918.514102. cited by applicant
.
Babbush, R., Gidney, C., Berry, D. W., Wiebe, N., McClean, J.,
Paler, A., Fowler, A., Neven, H. (2018). Encoding Electronic
Spectra in Quantum Circuits with Linear T Complexity. Physical
Review X, 8(4), 041015. https://doi.org/10.1103/PhysRevX.8.041015.
cited by applicant .
Ballance, C. J., Harty, T. P., Linke, N. M., Sepiol, M. A., Lucas,
D. M. (2016). High-Fidelity Quantum Logic Gates Using Trapped-Ion
Hyperfine Qubits. Physical Review Letters, 117(6), 060504.
https://doi.org/10.1103/PhysRevLett.117.060504. cited by applicant
.
Beauregard, S. (2003). Circuit for Shors algorithm using 2n+3
qubits. Quantum Information and Computation, 3(2), 175-185.
https://doi.org/https://dl.acm.org/doi/10.5555/2011517.2011525.
cited by applicant .
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V.,
Nam, Y., Perdomo-Ortiz, A. (2019). A generative modeling approach
for benchmarking and training shallow quantum circuits. Npj Quantum
Information, 5, 45. https://doi.org/10.1038/s41534-019-0157-8.
cited by applicant .
Bernstein, E., Vazirani, U., Comput, S. J. (1997). Quantum
Complexity Theory. SIAM Journal on Computing, 26(5), 1411-1473.
https://doi.org/10.1137/S0097539796300921. cited by applicant .
Blumel, R., Grzesiak, N., Nam, Y. (2019). Power-optimal, stabilized
entangling gate between trapped-ion qubits. ArXiV:1905.09292
[Quant-Ph]. cited by applicant .
Boyd, S. P., Vandenberghe, L. (2004). Convex optimization.
Cambridge University Press. cited by applicant .
Bravyi, S., Haah, J. (2012). Magic-state distillation with low
overhead. Physical Review A, 86(5), 052329.
https://doi.org/10.1103/PhysRevA.86.052329. cited by applicant
.
Calderon-Vargas, F. A., Barron, G. S., Deng, X.-H., Sigillito, A.
J., Barnes, E., Economou, S. E. (2019). Fast high-fidelity
entangling gates for spin qubits in Si double quantum dots.
Physical Review B, 100(3), 035304.
https://doi.org/10.1103/PhysRevB.100.035304. cited by applicant
.
Childs, A. M., Maslov, D., Nam, Y., Ross, N. J., Su, Y. (2018).
Toward the first quantum simulation with quantum speedup.
Proceedings of the National Academy of Sciences of the United
States of America, 115(38), 9456-9461.
https://doi.org/10.1073/pnas.1801723115. cited by applicant .
Choi, T., Debnath, S., Manning, T. A., Figgatt, C., Gong, Z. X.,
Duan, L. M., Monroe, C. (2014). Optimal quantum control of
multimode couplings between trapped ion qubits for scalable
entanglement. Physical Review Letters, 112(19), 190502.
https://doi.org/10.1103/PhysRevLett.112.190502. cited by applicant
.
Crooks, G. E. (2018). Performance of the Quantum Approximate
Optimization Algorithm on the Maximum Cut Problem. ArXiv:1811.08419
[Quant-Ph]. cited by applicant .
Draper, T. G., Kutin, S. A., Rains, E. M., Svore, K. M. (2006). A
Logarithmic-Depth Quantum Carry-Lookahead Adder. Quantum
Information Computation, 6(4), 351-369.
https://doi.org/10.5555/2012086.2012090. cited by applicant .
Feynman, R. P. (1982). Simulating Physics with Computers. In
International Journal of Theoretical Physics (vol. 21, Issue 6).
https://doi.org/10.1007/BF02650179. cited by applicant .
Figgatt, C., Ostrander, A., Linke, N. M., Landsman, K. A., Zhu, D.,
Maslov, D., Monroe, C. (2019). Parallel Entangling Operations on a
Universal Ion Trap Quantum Computer. Nature, 572, 368-372.
https://doi.org/10.1038/s41586-019-1427-5. cited by applicant .
Figgatt, C. M. (2018). Building and Programming a Universal Ion
Trap Quantum Computer. University of Maryland. cited by applicant
.
Gaebler, J. P., Tan, T. R., Lin, Y., Wan, Y., Bowler, R., Keith, A.
C., Glancy, S., Coakley, K., Knill, E., Leibfried, D., Wineland, D.
J. (2016). High-Fidelity Universal Gate Set for Be 9 + Ion Qubits.
Physical Review Letters, 117(6), 060505.
https://doi.org/10.1103/PhysRevLett.117.060505. cited by applicant
.
Gambetta, J. M., Motzoi, F., Merkel, S. T., Wilhelm, F. K. (2011).
Analytic control methods for high-fidelity unitary operations in a
weakly nonlinear oscillator. Physical Review A, 83(1), 012308.
https://doi.org/10.1103/PhysRevA.83.012308. cited by applicant
.
Gene M. Amdahl. (1967). Validity of the single processor approach
to achieving large scale computing capabilities. AFIPS Spring Joint
Computer Conference. cited by applicant .
Goemans, E. X., Williamson, D. P., Williamson, D. P., Goemans, M.
X. (1994). Improved Approximation Algorithms for Maximum Cut and
Satisfiability Problems Using Semidefinite Programming. Journal of
the Association for Computing Machinery, 42(6), 1115-1145.
https://doi.org/10.1145/227683.227684. cited by applicant .
Green, T. J., Biercuk, M. J. (2015). Phase-modulated decoupling and
error suppression in qubit-oscillator systems. Physical Review
Letters, 114(12), 120502.
https://doi.org/10.1103/PhysRevLett.114.120502. cited by applicant
.
Grover, L. K. (1997). Quantum Mechanics Helps in Searching for a
Needle in a Haystack. Physical Review Letters, 79(2), 325.
https://doi.org/https://doi.org/10.1103/PhysRevLett.79.325. cited
by applicant .
Grzesiak, N., Blumel, R., Wright, K., Beck, K. M., Pisenti, N. C.,
Li, M., Chaplin, V., Amini, J. M., Debnath, S., Chen, J. S., Nam,
Y. (2020). Efficient arbitrary simultaneously entangling gates on a
trapped-ion quantum computer. Nature Communications, 11, 2963.
https://doi.org/10.1038/s41467-020-16790-9. cited by applicant
.
Harrow, A. W., Hassidim, A., Lloyd, S. (2009). Quantum algorithm
for linear systems of equations. Physical Review Letters, 103(15),
150502. https://doi.org/10.1103/PhysRevLett.103.150502. cited by
applicant .
Harty, T. P., Allcock, D. T. C., Ballance, C. J., Guidoni, L.,
Janacek, H. A., Linke, N. M., Stacey, D. N., Lucas, D. M. (2014).
High-fidelity preparation, gates, memory, and readout of a
trapped-ion quantum bit. Physical Review Letters, 113(22), 220501.
https://doi.org/10.1103/PhysRevLett.113.220501. cited by applicant
.
Hewitt, E., Hewitt, R. E. (1979). The Gibbs-Wilbraham Phenomenon:
An Episode in Fourier Analysis. Archive for History of Exact
Sciences, 21, 129-160. https://doi.org/10.1007/BF00330404. cited by
applicant .
Lin, G.-D., Zhu, S.-L., Islam, R., Kim, K., Chang, M.-S.,
Korenblit, S., Monroe, C., & Duan, L.-M. (2009). Large-scale
quantum computation in an anharmonic linear ion trap. Europhysics
Letters, 86(6), 60004. https://doi.org/10.1209/0295-5075/86/60004.
cited by applicant .
Linke, N. M., Maslov, D., Roetteler, M., Debnath, S., Figgatt, C.,
Landsman, K. A., Wright, K., & Monroe, C. (2017). Experimental
comparison of two quantum computing architectures. Proceedings of
the National Academy of Sciences of the United States of America,
114(13), 3305-3310. https://doi.org/10.1073/pnas.1618020114. cited
by applicant .
Lloyd, S., Mohseni, M., & Rebentrost, P. (2014). Quantum
principal component analysis. Nature Physics, 10(9), 631-633.
https://doi.org/10.1038/NPHYS3029. cited by applicant .
Lu, Y., Zhang, S., Zhang, K., Chen, W., Shen, Y., Zhang, J., Zhang,
J.-N., & Kim, K. (2019). Scalable global entangling gates on
arbitrary ion qubits. Nature, 572, 363-367.
https://doi.org/10.1038/s41586-019-1428-4. cited by applicant .
Marquet, C., Schmidt-Kaler, F., & James, D. F. V. (2003).
Phonon-phonon interactions due to non-linear effects in a linear
ion trap. Applied Physics B, 76(3), 199-208.
https://doi.org/10.1007/s00340-003-1097-7. cited by applicant .
Maslov, D. (2016). Advantages of using relative-phase Toffoli gates
with an application to multiple control Toffoli optimization.
Physical Review A, 93(2), 022311.
https://doi.org/10.1103/PhysRevA.93.022311. cited by applicant
.
Maslov, D., Nam, Y., & Kim, J. (2019). An Outlook for Quantum
Computing [Point of View]. Proceedings of the IEEE, 107(1), 5-10.
https://doi.org/10.1109/JPROC.2018.2884353. cited by applicant
.
Merrill, J. T., & Brown, K. R. (2014). Progress in compensating
pulse sequences for quantum computation. In Sabre Kais (Ed.),
Quantum Information and Computation for Chemistry (pp. 241-294).
John Wiley & Sons. https://doi.org/10.1002/9781118742631.ch10.
cited by applicant .
Muller, M. M., Haakh, H. R., Calarco, T., Koch, C. P., &
Henkel, C. (2011). Prospects for fast Rydberg gates on an atom
chip. Quantum Information Processing, 10(6), 771-792.
https://doi.org/10.1007/s11128-011-0296-0. cited by applicant .
Nam, Y., & Maslov, D. (2019). Low-cost quantum circuits for
classically intractable instances of the Hamiltonian dynamics
simulation problem. Npj Quantum Information, 5, 44.
https://doi.org/10.1038/s41534-019-0152-0. cited by applicant .
Nam, Y., Su, Y., & Maslov, D. (2020). Approximate quantum
Fourier transform with O(n log(n)) T gates. Npj Quantum
Information, 6, 26. https://doi.org/10.1038/s41534-020-0257-5.
cited by applicant .
Nielsen, M. A., & Chuang, I. L. (2010). Quantum computation and
quantum information. Cambridge University Press.
https://doi.org/10.1017/CBO9780511976667. cited by applicant .
O'Gorman, J., & Campbell, E. T. (2017). Quantum computation
with realistic magic-state factories. Physical Review A, 95(3),
032338. https://doi.org/10.1103/PhysRevA.95.032338. cited by
applicant .
Orus, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for
finance: overview and prospects. Reviews in Physics , 4, 100028.
https://doi.org/10.1016/j.revip.2019.100028. cited by applicant
.
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M.
(2017). Elucidating reaction mechanisms on quantum computers.
Proceedings of the National Academy of Sciences of the United
States of America, 114(29), 7555-7560.
https://doi.org/10.1073/pnas.1619152114. cited by applicant .
Shende, V. V., Markov, I. L., & Bullock, S. S. (2004). Minimal
universal two-qubit controlled-NOT-based circuits. Physical Review
A, 69(6), 062321. https://doi.org/10.1103/PhysRevA.69.062321. cited
by applicant .
Shor, P. W. (1999). Polynomial-Time Algorithms for Prime
Factorization and Discrete Logarithms on a. SIAM Review, 41(2),
303-332. cited by applicant .
Sorensen, A., & Molmer, K. (1999). Quantum Computation with
Ions in Thermal Motion. Physical Review Letters, 82(9), 1971.
https://doi.org/10.1103/PhysRevLett.82.1971. cited by applicant
.
Sporl, A., Schulte-Herbruggen, T., Glaser, S. J., Bergholm, V.,
Storcz, M. J., Ferber, J., & Wilhelm, F. K. (2007). Optimal
control of coupled Josephson qubits. Physical Review A, 75(1),
012302. https://doi.org/10.1103/PhysRevA.75.012302. cited by
applicant .
Theis, L. S., Motzoi, F., Wilhelm, F. K., & Saffman, M. (2016).
High-fidelity Rydberg-blockade entangling gate using shaped,
analytic pulses. Physical Review A, 94(3), 032306.
https://doi.org/10.1103/PhysRevA.94.032306. cited by applicant
.
Van Dam, W., Hallgren, S., & Ip, L. (2006). Quantum algorithms
for some hidden shift problems. SIAM Journal on Computing, 36(3),
763-778. https://doi.org/10.1137/S009753970343141X. cited by
applicant.
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Primary Examiner: Gheyas; Syed I
Attorney, Agent or Firm: Patterson + Sheridan LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit to U.S. Provisional Application
No. 62/852,264, filed May 23, 2019, which is incorporated by
reference herein.
Claims
The invention claimed is:
1. A method of performing computation in a hybrid quantum-classical
computing system comprising a classical computer and a quantum
processor, comprising: computing, by a classical computer, a model
Hamiltonian onto which a selected problem is mapped, wherein the
model Hamiltonian comprises a plurality of sub-Hamiltonians;
setting a quantum processor in an initial state, wherein the
quantum processor comprises a plurality of trapped ions, each of
which has two frequency-separated states defining a qubit;
transforming the quantum processor from the initial state to a
trial state based on each of the plurality of sub-Hamiltonians and
an initial set of variational parameters by applying a reduced
trial state preparation circuit to the quantum processor; measuring
an expectation value of each of the plurality of sub-Hamiltonians
on the quantum processor; and determining, by the classical
computer, if a difference between the measured expectation value of
the model Hamiltonian is more or less than a predetermined value,
wherein the classical computer either: selects another set of
variational parameters based on a classical optimization method if
it is determined that the difference is more than the predetermined
value and then: sets the quantum processor in the initial state,
transforms the quantum processor from the initial state to a new
trial state based on each of the plurality of sub-Hamiltonians and
the another set of variational parameters by applying a new reduced
trial state preparation circuit to the quantum processor, and
measures an expectation value of the each of the plurality of
sub-Hamiltonians on the quantum processor after transforming the
quantum processor to the new trial state; or outputs the measured
expectation value of the model Hamiltonian as an optimized solution
to the selected problem if it is determined that the difference is
less than the predetermined value.
2. The method according to claim 1, wherein the reduced trial state
preparation circuit does not include gate operations that do not
influence the expectation value of the each of the plurality of
sub-Hamiltonians.
3. The method according to claim 1, wherein if it is determined
that the difference is more than the predetermined value, the
determining step is repeated.
4. The method according to claim 1, wherein the problem to be
solved is finding a lowest energy of a many-particle quantum
system.
5. The method according to claim 4, further comprising: selecting,
by the classical computer, the initial set of variational
parameters, wherein the initial set of variational parameters is
selected randomly.
6. The method according to claim 4, wherein setting the quantum
processor in the initial state comprising setting the plurality of
trapped ions in the quantum processor in an approximate state of
the many-particle quantum system that is calculated by the
classical computer.
7. The method according to claim 1, wherein the problem to be
solved is a combinatorial optimization problem.
8. The method according to claim 7, further comprising: selecting,
by the classical computer, the initial set of variational
parameters, wherein the initial set of variational parameters is
selected by the classical computer randomly.
9. The method according to claim 7, wherein setting the quantum
processor in the initial state comprising setting, by a system
controller, each trapped ion in the quantum processor in a
superposition of the two frequency-separated states.
10. A hybrid quantum-classical computing system, comprising: a
quantum processor comprising a group of trapped ions, each of the
trapped ions having two hyperfine states defining a qubit; one or
more lasers configured to emit a laser beam, which is provided to
trapped ions in the quantum processor; and a classical computer
configured to: select a problem to be solved; compute a model
Hamiltonian onto which the selected problem is mapped, wherein the
model Hamiltonian comprises a plurality of sub-Hamiltonians; select
a set of variational parameters; set the quantum processor in an
initial state; transform the quantum processor from the initial
state to a trial state based on each of the plurality of
sub-Hamiltonians and an initial set of variational parameters by
applying a reduced trial state preparation circuit to the quantum
processor; measure an expectation value of each of the plurality of
sub-Hamiltonians on the quantum processor; and determine if a
difference between the measured expectation value of the model
Hamiltonian is more or less than a predetermined value, wherein the
classical computer either: selects another set of variational
parameters based on a classical optimization method if it is
determined that the difference is more than the predetermined value
and then: sets the quantum processor in the initial state,
transforms the quantum processor from the initial state to a new
trial state based on each of the plurality of sub-Hamiltonians and
the another set of variational parameters by applying a new reduced
trial state preparation circuit to the quantum processor, and
measures an expectation value of each of the plurality of
sub-Hamiltonians on the quantum processor after transforming the
quantum processor to the new trial state; or outputs the measured
expectation value of the model Hamiltonian as an optimized solution
to the selected problem if it is determined that the difference is
less than the predetermined value.
11. The hybrid quantum-classical computing system according to
claim 10, wherein the reduced trial state preparation circuit does
not include gate operations that do not influence the expectation
value of the each of the plurality of sub-Hamiltonians.
12. The hybrid quantum-classical computing system according to
claim 10, wherein if it is determined that the difference is more
than the predetermined value, the classical computer repeats the
determining step.
13. The hybrid quantum-classical computing system according to
claim 10, wherein the problem to be solved is finding a lowest
energy of a many-particle quantum system.
14. The hybrid quantum-classical computing system according to
claim 13, wherein the classical computer further selects the
initial set of variational parameters randomly.
15. The hybrid quantum-classical computing system according to
claim 13, wherein the initial state is an approximate state of the
many-particle quantum system that is calculated by the classical
computer.
16. The hybrid quantum-classical computing system according to
claim 10, wherein the problem to be solved is a combinatorial
optimization problem.
17. The hybrid quantum-classical computing system according to
claim 16, wherein the classical computer selects the initial set of
variational parameters randomly.
18. The hybrid quantum-classical computing system according to
claim 16, wherein the initial state is a superposition of the two
hyperfine states.
19. A hybrid quantum-classical computing system comprising
non-volatile memory having a number of instructions stored therein
which, when executed by one or more processors, causes the hybrid
quantum-classical computing system to perform operations
comprising: computing a model Hamiltonian onto which a selected
problem is mapped, wherein the model Hamiltonian comprises a
plurality of sub-Hamiltonians; setting a quantum processor in an
initial state, wherein the quantum processor comprises a plurality
of trapped ions, each of which has two frequency-separated states
defining a qubit; transforming the quantum processor from the
initial state to a trial state based on each of the plurality of
sub-Hamiltonians and an initial set of variational parameters by
applying a reduced trial state preparation circuit to the quantum
processor; measuring an expectation value of each of the plurality
of sub-Hamiltonians on the quantum processor; and determining if a
difference between the measured expectation value of the model
Hamiltonian is more or less than a predetermined value, wherein the
instructions further cause the hybrid quantum-classical computing
system to either: select another set of variational parameters
based on a classical optimization method if it is determined that
the difference is more than the predetermined value and then: set
the quantum processor in the initial state, transform the quantum
processor from the initial state to a new trial state based on each
of the plurality of sub-Hamiltonians and the another set of
variational parameters by applying a new reduced trial state
preparation circuit to the quantum processor, and measure an
expectation value of the each of the plurality of sub-Hamiltonians
on the quantum processor after transforming the quantum processor
to the new trial state; or output the measured expectation value of
the model Hamiltonian as an optimized solution to the selected
problem if it is determined that the difference is less than the
predetermined value.
20. The hybrid quantum-classical computing system according to
claim 19, wherein the reduced trial state preparation circuit does
not include gate operations that do not influence the expectation
value of the each of the plurality of sub-Hamiltonians.
Description
BACKGROUND
Field
The present disclosure generally relates to a method of performing
computation in a hybrid quantum-classical computing system, and
more specifically, to a method of solving an optimization problem
in a hybrid computing system that includes a classical computer and
quantum computer that includes a group of trapped ions.
Description of the Related Art
In quantum computing, quantum bits or qubits, which are analogous
to bits representing a "0" and a "1" in a classical (digital)
computer, are required to be prepared, manipulated, and measured
(read-out) with near perfect control during a computation process.
Imperfect control of the qubits leads to errors that can accumulate
over the computation process, limiting the size of a quantum
computer that can perform reliable computations.
Among physical systems upon which it is proposed to build
large-scale quantum computers, is a group of ions (e.g., charged
atoms), which are trapped and suspended in vacuum by
electromagnetic fields. The ions have internal hyperfine states
which are separated by frequencies in the several GHz range and can
be used as the computational states of a qubit (referred to as
"qubit states"). These hyperfine states can be controlled using
radiation provided from a laser, or sometimes referred to herein as
the interaction with laser beams. The ions can be cooled to near
their motional ground states using such laser interactions. The
ions can also be optically pumped to one of the two hyperfine
states with high accuracy (preparation of qubits), manipulated
between the two hyperfine states (single-qubit gate operations) by
laser beams, and their internal hyperfine states detected by
fluorescence upon application of a resonant laser beam (read-out of
qubits). A pair of ions can be controllably entangled (two-qubit
gate operations) by qubit-state dependent force using laser pulses
that couple the ions to the collective motional modes of a group of
trapped ions, which arise from their Coulombic interaction between
the ions. In general, entanglement occurs when pairs or groups of
ions (or particles) are generated, interact, or share spatial
proximity in ways such that the quantum state of each ion cannot be
described independently of the quantum state of the others, even
when the ions are separated by a large distance.
In current state-of-the-art quantum computers, control of qubits is
imperfect (noisy) and the number of qubits used in these quantum
computers generally range from a hundred qubits to thousands of
qubits. The number of quantum gates that can be used in such a
quantum computer (referred to as a "noisy intermediate-scale
quantum device" or "NISQ device") to construct circuits to run an
algorithm within a controlled error rate is limited due to the
noise.
For solving some optimization problems, a NISQ device having
shallow circuits (with small number of gate operations to be
executed in time-sequence) can be used in combination with a
classical computer (referred to as a hybrid quantum-classical
computing system). In particular, finding low-energy states of a
many-particle quantum system, such as large molecules, or in
finding an approximate solution to combinatorial optimization
problems, a quantum subroutine, which is run on a NISQ device, can
be run as part of a classical optimization routine, which is run on
a classical computer. The classical computer (also referred to as a
"classical optimizer") instructs a controller to prepare the NISQ
device (also referred to as a "quantum processor") in an N-qubit
state, execute quantum gate operations, and measure an outcome of
the quantum processor. Subsequently, the classical optimizer
instructs the controller to prepare the quantum processor in a
slightly different N-qubit state, and repeats execution of the gate
operation and measurement of the outcome. This cycle is repeated
until the approximate solution can be extracted. Such hybrid
quantum-classical computing system having an NISQ device may
outperform classical computers in finding low-energy states of a
many-particle quantum system and in finding approximate solutions
to such combinatorial optimization problems. However, the number of
quantum gate operations required within the quantum routine
increases rapidly as the problem size increases, leading to
accumulated errors in the NISQ device and causing the outcomes of
these processes to be not reliable.
Therefore, there is a need for a procedure to construct shallow
circuits that require a minimum number of quantum gate operations
to perform computation and thus reduce noise in a hybrid
quantum-classical computing system.
SUMMARY
A method of performing computation in a hybrid quantum-classical
computing system includes computing, by a classical computer, a
model Hamiltonian including a plurality of sub-Hamiltonian onto
which a selected problem is mapped, setting a quantum processor in
an initial state, where the quantum processor comprises a plurality
of trapped ions, each of which has two frequency-separated states
defining a qubit, transforming the quantum processor from the
initial state to a trial state based on each of the plurality of
sub-Hamiltonians and an initial set of variational parameters by
applying a reduced trial state preparation circuit to the quantum
processor, measuring an expectation value of each of the plurality
of sub-Hamiltonians on the quantum processor, and determining, by
the classical computer, if a difference between the measured
expectation value of the model Hamiltonian is more or less than a
predetermined value. If it is determined that the difference is
more than the predetermined value, the classical computer either
selects another set of variational parameters based on a classical
optimization method, sets the quantum processor in the initial
state, transforms the quantum processor from the initial state to a
new trial state based on each of the plurality of sub-Hamiltonians
and the another set of variational parameters by applying a new
reduced trial state preparation circuit to the quantum processor,
and measures an expectation value of the each of the plurality of
sub-Hamiltonians on the quantum processor after transforming the
quantum processor to the new trial state. If it is determined that
the difference is less than the predetermined value, the classical
computer outputs the measured expectation value of the model
Hamiltonian as an optimized solution to the selected problem.
BRIEF DESCRIPTION OF THE DRAWINGS
So that the manner in which the above-recited features of the
present disclosure can be understood in detail, a more particular
description of the disclosure, briefly summarized above, may be had
by reference to embodiments, some of which are illustrated in the
appended drawings. It is to be noted, however, that the appended
drawings illustrate only typical embodiments of this disclosure and
are therefore not to be considered limiting of its scope, for the
disclosure may admit to other equally effective embodiments.
FIG. 1 is a schematic partial view of an ion trap quantum computing
system according to one embodiment.
FIG. 2 depicts a schematic view of an ion trap for confining ions
in a group according to one embodiment.
FIG. 3 depicts a schematic energy diagram of each ion in a group of
trapped ions according to one embodiment.
FIG. 4 depicts a qubit state of an ion represented as a point on a
surface of the Bloch sphere.
FIGS. 5A, 5B, and 5C depict a few schematic collective transverse
motional mode structures of a group of five trapped ions.
FIGS. 6A and 6B depict schematic views of motional sideband
spectrum of each ion and a motional mode according to one
embodiment.
FIG. 7 depicts an overall hybrid quantum-classical computing system
for obtaining a solution to an optimization problem by Variational
Quantum Eigensolver (VQE) algorithm or Quantum Approximate
Optimization Algorithm (QAOA) according to one embodiment.
FIG. 8 depicts a flowchart illustrating a method of obtaining a
solution to an optimization problem by Variational Quantum
Eigensolver (VQE) algorithm or Quantum Approximate Optimization
Algorithm (QAOA) according to one embodiment.
FIG. 9A illustrates a trial state preparation circuit according to
one embodiment.
FIGS. 9B and 9C illustrate reduced trial state preparation circuits
according to one embodiment.
To facilitate understanding, identical reference numerals have been
used, where possible, to designate identical elements that are
common to the figures. In the figures and the following
description, an orthogonal coordinate system including an X-axis, a
Y-axis, and a Z-axis is used. The directions represented by the
arrows in the drawing are assumed to be positive directions for
convenience. It is contemplated that elements disclosed in some
embodiments may be beneficially utilized on other implementations
without specific recitation.
DETAILED DESCRIPTION
Embodiments described herein are generally related to a method and
a system for performing a computation using a hybrid
quantum-classical computing system, and, more specifically, to
providing an approximate solution to an optimization problem using
a hybrid quantum-classical computing system that includes a group
of trapped ions.
A hybrid quantum-classical computing system that is able to provide
a solution to a combinatorial optimization problem may include a
classical computer, a system controller, and a quantum processor.
In some embodiments, the system controller is housed within the
classical computer. The classical computer performs supporting and
system control tasks including selecting a combinatorial
optimization problem to be run by use of a user interface, running
a classical optimization routine, translating the series of logic
gates into laser pulses to apply on the quantum processor, and
pre-calculating parameters that optimize the laser pulses by use of
a central processing unit (CPU). A software program for performing
the tasks is stored in a non-volatile memory within the classical
computer.
The quantum processor includes trapped ions that are coupled with
various hardware, including lasers to manipulate internal hyperfine
states (qubit states) of the trapped ions and an acousto-optic
modulator to read-out the internal hyperfine states (qubit states)
of the trapped ions. The system controller receives from the
classical computer instructions for controlling the quantum
processor, controls various hardware associated with controlling
any and all aspects used to run the instructions for controlling
the quantum processor, and returns a read-out of the quantum
processor and thus output of results of the computation(s) to the
classical computer.
The methods and systems described herein include an efficient
method for constructing quantum gate operations executed by the
quantum processor in solving a problem in a hybrid
quantum-classical computing system.
General Hardware Configurations
FIG. 1 is a schematic partial view of an ion trap quantum computing
system, or system 100, according to one embodiment. The system 100
includes a classical (digital) computer 102, a system controller
104 and a quantum processor that is a group 106 of trapped ions
(i.e., five shown) that extend along the Z-axis. The classical
computer 102 includes a central processing unit (CPU), memory, and
support circuits (or I/O). The memory is connected to the CPU, and
may be one or more of a readily available memory, such as a
read-only memory (ROM), a random access memory (RAM), floppy disk,
hard disk, or any other form of digital storage, local or remote.
Software instructions, algorithms and data can be coded and stored
within the memory for instructing the CPU. The support circuits
(not shown) are also connected to the CPU for supporting the
processor in a conventional manner. The support circuits may
include conventional cache, power supplies, clock circuits,
input/output circuitry, subsystems, and the like.
An imaging objective 108, such as an objective lens with a
numerical aperture (NA), for example, of 0.37, collects
fluorescence along the Y-axis from the ions and maps each ion onto
a multi-channel photo-multiplier tube (PMT) 110 for measurement of
individual ions. Non-copropagating Raman laser beams from a laser
112, which are provided along the X-axis, perform operations on the
ions. A diffractive beam splitter 114 creates an array of static
Raman beams 116 that are individually switched using a
multi-channel acousto-optic modulator (AOM) 118 and is configured
to selectively act on individual ions. A global Raman laser beam
120 illuminates all ions at once. The system controller (also
referred to as a "RF controller") 104 controls the AOM 118 and thus
controls laser pulses to be applied to trapped ions in the group
106 of trapped ions. The system controller 104 includes a central
processing unit (CPU) 122, a read-only memory (ROM) 124, a random
access memory (RAM) 126, a storage unit 128, and the like. The CPU
122 is a processor of the system controller 104. The ROM 124 stores
various programs and the RAM 126 is the working memory for various
programs and data. The storage unit 128 includes a nonvolatile
memory, such as a hard disk drive (HDD) or a flash memory, and
stores various programs even if power is turned off. The CPU 122,
the ROM 124, the RAM 126, and the storage unit 128 are
interconnected via a bus 130. The system controller 104 executes a
control program which is stored in the ROM 124 or the storage unit
128 and uses the RAM 126 as a working area. The control program
will include software applications that include program code that
may be executed by processor in order to perform various
functionalities associated with receiving and analyzing data and
controlling any and all aspects of the methods and hardware used to
create the ion trap quantum computer system 100 discussed
herein.
FIG. 2 depicts a schematic view of an ion trap 200 (also referred
to as a Paul trap) for confining ions in the group 106 according to
one embodiment. The confining potential is exerted by both static
(DC) voltage and radio frequency (RF) voltages. A static (DC)
voltage V.sub.S is applied to end-cap electrodes 210 and 212 to
confine the ions along the Z-axis (also referred to as an "axial
direction" or a "longitudinal direction"). The ions in the group
106 are nearly evenly distributed in the axial direction due to the
Coulomb interaction between the ions. In some embodiments, the ion
trap 200 includes four hyperbolically-shaped electrodes 202, 204,
206, and 208 extending along the Z-axis.
During operation, a sinusoidal voltage V.sub.1 (with an amplitude
V.sub.RF/2) is applied to an opposing pair of the electrodes 202,
204 and a sinusoidal voltage V.sub.2 with a phase shift of
180.degree. from the sinusoidal voltage V.sub.1 (and the amplitude
V.sub.RF/2) is applied to the other opposing pair of the electrodes
206, 208 at a driving frequency .omega..sub.RF, generating a
quadrupole potential. In some embodiments, a sinusoidal voltage is
only applied to one opposing pair of the electrodes 202, 204, and
the other opposing pair 206, 208 is grounded. The quadrupole
potential creates an effective confining force in the X-Y plane
perpendicular to the Z-axis (also referred to as a "radial
direction" or "transverse direction") for each of the trapped ions,
which is proportional to a distance from a saddle point (i.e., a
position in the axial direction (Z-direction)) at which the RF
electric field vanishes. The motion in the radial direction (i.e.,
direction in the X-Y plane) of each ion is approximated as a
harmonic oscillation (referred to as secular motion) with a
restoring force towards the saddle point in the radial direction
and can be modeled by spring constants k.sub.x and k.sub.y,
respectively, as is discussed in greater detail below. In some
embodiments, the spring constants in the radial direction are
modeled as equal when the quadrupole potential is symmetric in the
radial direction. However, undesirably in some cases, the motion of
the ions in the radial direction may be distorted due to some
asymmetry in the physical trap configuration, a small DC patch
potential due to inhomogeneity of a surface of the electrodes, or
the like and due to these and other external sources of distortion
the ions may lie off-center from the saddle points.
FIG. 3 depicts a schematic energy diagram 300 of each ion in the
group 106 of trapped ions according to one embodiment. In one
example, each ion may be a positive Ytterbium ion,
.sup.171Yb.sup.+, which has the .sup.2S.sub.1/2 hyperfine states
(i.e., two electronic states) with an energy split corresponding to
a frequency difference (referred to as a "carrier frequency") of
.omega..sub.01/2.pi.=12.642821 GHz. A qubit is formed with the two
hyperfine states, denoted as |0 and |1, where the hyperfine ground
state (i.e., the lower energy state of the .sup.2S.sub.1/2
hyperfine states) is chosen to represent |0. Hereinafter, the terms
"hyperfine states," "internal hyperfine states," and "qubits" may
be interchangeably used to represent |0 and |1. Each ion may be
cooled (i.e., kinetic energy of the ion may be reduced) to near the
motional ground state |0.sub.m for any motional mode m with no
phonon excitation (i.e., n.sub.ph=0) by known laser cooling
methods, such as Doppler cooling or resolved sideband cooling, and
then the qubit state prepared in the hyperfine ground state |0 by
optical pumping. Here, |0 represents the individual qubit state of
a trapped ion whereas |0.sub.m with the subscript m denotes the
motional ground state for a motional mode m of a group 106 of
trapped ions.
An individual qubit state of each trapped ion may be manipulated
by, for example, a mode-locked laser at 355 nanometers (nm) via the
excited .sup.2P.sub.1/2 level (denoted as |e). As shown in FIG. 3,
a laser beam from the laser may be split into a pair of
non-copropagating laser beams (a first laser beam with frequency
.omega..sub.1 and a second laser beam with frequency .omega..sub.2)
in the Raman configuration, and detuned by a one-photon transition
detuning frequency .DELTA.=.omega..sub.1-.omega..sub.0e with
respect to the transition frequency .omega..sub.0e between |0 and
|e, as illustrated in FIG. 3. A two-photon transition detuning
frequency .delta. includes adjusting the amount of energy that is
provided to the trapped ion by the first and second laser beams,
which when combined is used to cause the trapped ion to transfer
between the hyperfine states |0 and |1. When the one-photon
transition detuning frequency .DELTA. is much larger than a
two-photon transition detuning frequency (also referred to simply
as "detuning frequency")
.delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01 (hereinafter
denoted as .+-..mu., .mu. being a positive value), single-photon
Rabi frequencies .OMEGA..sub.0e(t) and .OMEGA..sub.1e(t) (which are
time-dependent, and are determined by amplitudes and phases of the
first and second laser beams), at which Rabi flopping between
states |0 and |e and between states |1 and |e respectively occur,
and a spontaneous emission rate from the excited state |e, Rabi
flopping between the two hyperfine states |0 and |1 (referred to as
a "carrier transition") is induced at the two-photon Rabi frequency
.OMEGA.(t). The two-photon Rabi frequency .OMEGA.(t) has an
intensity (i.e., absolute value of amplitude) that is proportional
to .OMEGA..sub.0e.OMEGA..sub.1e/2.DELTA., where .OMEGA..sub.0e and
.OMEGA..sub.1e are the single-photon Rabi frequencies due to the
first and second laser beams, respectively. Hereinafter, this set
of non-copropagating laser beams in the Raman configuration to
manipulate internal hyperfine states of qubits (qubit states) may
be referred to as a "composite pulse" or simply as a "pulse," and
the resulting time-dependent pattern of the two-photon Rabi
frequency .OMEGA.(t) may be referred to as an "amplitude" of a
pulse or simply as a "pulse," which are illustrated and further
described below. The detuning frequency
.delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01 may be referred
to as detuning frequency of the composite pulse or detuning
frequency of the pulse. The amplitude of the two-photon Rabi
frequency .OMEGA.(t), which is determined by amplitudes of the
first and second laser beams, may be referred to as an "amplitude"
of the composite pulse.
It should be noted that the particular atomic species used in the
discussion provided herein is just one example of atomic species
which has stable and well-defined two-level energy structures when
ionized and an excited state that is optically accessible, and thus
is not intended to limit the possible configurations,
specifications, or the like of an ion trap quantum computer
according to the present disclosure. For example, other ion species
include alkaline earth metal ions (Be.sup.+, Ca.sup.+, Sr.sup.+,
Mg+, and Ba.sup.+) or transition metal ions (Zn.sup.+, Hg.sup.+,
Cd.sup.+).
FIG. 4 is provided to help visualize a qubit state of an ion is
represented as a point on a surface of the Bloch sphere 400 with an
azimuthal angle .PHI. and a polar angle .theta.. Application of the
composite pulse as described above, causes Rabi flopping between
the qubit state |0 (represented as the north pole of the Bloch
sphere) and |1 (the south pole of the Bloch sphere) to occur.
Adjusting time duration and amplitudes of the composite pulse flips
the qubit state from |0 to |1 (i.e., from the north pole to the
south pole of the Bloch sphere), or the qubit state from |1 to |0
(i.e., from the south pole to the north pole of the Bloch sphere).
This application of the composite pulse is referred to as a
".pi.-pulse". Further, by adjusting time duration and amplitudes of
the composite pulse, the qubit state |0 may be transformed to a
superposition state |0+|1, where the two qubit states |0 and |1 are
added and equally-weighted in-phase (a normalization factor of the
superposition state is omitted hereinafter without loss of
generality) and the qubit state |1 to a superposition state |0-|1,
where the two qubit states |0 and |1 are added equally-weighted but
out of phase. This application of the composite pulse is referred
to as a ".pi./2-pulse". More generally, a superposition of the two
qubits states |0 and |1 that are added and equally-weighted is
represented by a point that lies on the equator of the Bloch
sphere. For example, the superposition states |0.+-.|1 correspond
to points on the equator with the azimuthal angle .PHI. being zero
and .pi., respectively. The superposition states that correspond to
points on the equator with the azimuthal angle .PHI. are denoted as
|0+e.sup.i.PHI.|1 (e.g., |0.+-.i|1 for .PHI.=.+-..pi./2).
Transformation between two points on the equator (i.e., a rotation
about the Z-axis on the Bloch sphere) can be implemented by
shifting phases of the composite pulse.
Entanglement Formation
FIGS. 5A, 5B, and 5C depict a few schematic structures of
collective transverse motional modes (also referred to simply as
"motional mode structures") of a group 106 of five trapped ions,
for example. Here, the confining potential due to a static voltage
V.sub.S applied to the end-cap electrodes 210 and 212 is weaker
compared to the confining potential in the radial direction. The
collective motional modes of the group 106 of trapped ions in the
transverse direction are determined by the Coulomb interaction
between the trapped ions combined with the confining potentials
generated by the ion trap 200. The trapped ions undergo collective
transversal motions (referred to as "collective transverse motional
modes," "collective motional modes," or simply "motional modes"),
where each mode has a distinct energy (or equivalently, a
frequency) associated with it. A motional mode having the m-th
lowest energy is hereinafter referred to as |n.sub.ph.sub.m, where
n.sub.ph denotes the number of motional quanta (in units of energy
excitation, referred to as phonons) in the motional mode, and the
number of motional modes M in a given transverse direction is equal
to the number of trapped ions N in the group 106. FIGS. 5A-5C
schematically illustrates examples of different types of collective
transverse motional modes that may be experienced by five trapped
ions that are positioned in a group 106. FIG. 5A is a schematic
view of a common motional mode |n.sub.ph.sub.M having the highest
energy, where M is the number of motional modes. In the common
motional mode |n.sub.M, all ions oscillate in phase in the
transverse direction. FIG. 5B is a schematic view of a tilt
motional mode |n.sub.ph.sub.M-1 which has the second highest
energy. In the tilt motional mode, ions on opposite ends move out
of phase in the transverse direction (i.e., in opposite
directions). FIG. 5C is a schematic view of a higher-order motional
mode |n.sub.ph.sub.M-3 which has a lower energy than that of the
tilt motional mode |n.sub.ph.sub.M-1, and in which the ions move in
a more complicated mode pattern.
It should be noted that the particular configuration described
above is just one among several possible examples of a trap for
confining ions according to the present disclosure and does not
limit the possible configurations, specifications, or the like of
traps according to the present disclosure. For example, the
geometry of the electrodes is not limited to the hyperbolic
electrodes described above. In other examples, a trap that
generates an effective electric field causing the motion of the
ions in the radial direction as harmonic oscillations may be a
multi-layer trap in which several electrode layers are stacked and
an RF voltage is applied to two diagonally opposite electrodes, or
a surface trap in which all electrodes are located in a single
plane on a chip. Furthermore, a trap may be divided into multiple
segments, adjacent pairs of which may be linked by shuttling one or
more ions, or coupled by photon interconnects. A trap may also be
an array of individual trapping regions arranged closely to each
other on a micro-fabricated ion trap chip. In some embodiments, the
quadrupole potential has a spatially varying DC component in
addition to the RF component described above.
In an ion trap quantum computer, the motional modes may act as a
data bus to mediate entanglement between two qubits and this
entanglement is used to perform an XX gate operation. That is, each
of the two qubits is entangled with the motional modes, and then
the entanglement is transferred to an entanglement between the two
qubits by using motional sideband excitations, as described below.
FIGS. 6A and 6B schematically depict views of a motional sideband
spectrum for an ion in the group 106 in a motional mode
|n.sub.ph.sub.M having frequency .omega..sub.m according to one
embodiment. As illustrated in FIG. 6B, when the detuning frequency
of the composite pulse is zero (i.e., a frequency difference
between the first and second laser beams is tuned to the carrier
frequency, .delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01=0),
simple Rabi flopping between the qubit states |0 and |1 (carrier
transition) occurs. When the detuning frequency of the composite
pulse is positive (i.e., the frequency difference between the first
and second laser beams is tuned higher than the carrier frequency,
.delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01=.mu.>0,
referred to as a blue sideband), Rabi flopping between combined
qubit-motional states |0|n.sub.ph.sub.m and |1|n.sub.ph+1.sub.m
occurs (i.e., a transition from the m-th motional mode with
n-phonon excitations denoted by |n.sub.ph.sub.m to the m-th
motional mode with (n.sub.ph+1)-phonon excitations denoted by
|n.sub.ph+1.sub.m occurs when the qubit state |0 flips to |1). When
the detuning frequency of the composite pulse is negative (i.e.,
the frequency difference between the first and second laser beams
is tuned lower than the carrier frequency by the frequency
.omega..sub.m of the motional mode |n.sub.ph.sub.m,
.delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01=-.mu.<0,
referred to as a red sideband), Rabi flopping between combined
qubit-motional states |0|n.sub.ph.sub.m and |1|n.sub.ph-1.sub.m
occurs (i.e., a transition from the motional mode |n.sub.ph.sub.m
to the motional mode |n.sub.ph-1.sub.m with one less phonon
excitations occurs when the qubit state |0 flips to |1). A
.pi./2-pulse on the blue sideband applied to a qubit transforms the
combined qubit-motional state |0|n.sub.ph.sub.m into a
superposition of |0|n.sub.ph.sub.m and |1|n.sub.ph+1.sub.m. A
.pi./2-pulse on the red sideband applied to a qubit transforms the
combined qubit-motional |0|n.sub.ph.sub.m into a superposition of
|0|n.sub.ph.sub.m and |1|n.sub.ph-1.sub.m. When the two-photon Rabi
frequency .OMEGA.(t) is smaller as compared to the detuning
frequency
.delta.=.omega..sub.1-.omega..sub.2-.omega..sub.01=.+-..mu., the
blue sideband transition or the red sideband transition may be
selectively driven. Thus, qubit states of a qubit can be entangled
with a desired motional mode by applying the right type of pulse,
such as a .pi./2-pulse, which can be subsequently entangled with
another qubit, leading to an entanglement between the two qubits
that is needed to perform an XX-gate operation in an ion trap
quantum computer.
By controlling and/or directing transformations of the combined
qubit-motional states as described above, an XX-gate operation may
be performed on two qubits (i-th and j-th qubits). In general, the
XX-gate operation (with maximal entanglement) respectively
transforms two-qubit states |0.sub.i|0.sub.j, |0.sub.i|1.sub.j,
|1.sub.i|0.sub.j, and as follows:
|0.sub.i|0.sub.j.fwdarw.|0.sub.i|0.sub.j-i|1.sub.i|1.sub.j
|0.sub.i|1.sub.j.fwdarw.|0.sub.i|1.sub.j-i|1.sub.i|0.sub.j
|1.sub.i|0.sub.j.fwdarw.-i|0.sub.i|1.sub.j+|1.sub.i|0.sub.j
|1.sub.i|1.sub.j.fwdarw.-i|0.sub.i|1.sub.j+|1.sub.i|1.sub.j. For
example, when the two qubits (i-th and j-th qubits) are both
initially in the hyperfine ground state |0 (denoted as
|0.sub.i|0.sub.j) and subsequently a .pi./2-pulse on the blue
sideband is applied to the i-th qubit, the combined state of the
i-th qubit and the motional mode |0.sub.i|n.sub.ph.sub.m is
transformed into a superposition of |0.sub.i|n.sub.ph.sub.m and
|1.sub.i|n.sub.ph+1.sub.m, and thus the combined state of the two
qubits and the motional mode is transformed into a superposition of
|0.sub.i|0.sub.j|n.sub.ph.sub.m and
|1.sub.i|0.sub.j|n.sub.ph+1.sub.m. When a .pi./2-pulse on the red
sideband is applied to the j-th qubit, the combined state of the
j-th qubit and the motional mode |0.sub.j|n.sub.ph.sub.m is
transformed to a superposition of |0.sub.j|n.sub.ph.sub.m and
|1.sub.j|n.sub.ph-1.sub.m and the combined state
|0.sub.j|n.sub.ph+1.sub.m is transformed into a superposition of
|0.sub.j|n.sub.ph+1.sub.m and.
Thus, applications of a .pi./2-pulse on the blue sideband on the
i-th qubit and a .pi./2-pulse on the red sideband on the j-th qubit
may transform the combined state of the two qubits and the motional
mode |0.sub.i|0.sub.j|n.sub.ph.sub.m into a superposition of
|0.sub.i|0.sub.j|n.sub.ph.sub.m and
|1.sub.i|1.sub.j|n.sub.ph.sub.m, the two qubits now being in an
entangled state. For those of ordinary skill in the art, it should
be clear that two-qubit states that are entangled with motional
mode having a different number of phonon excitations from the
initial number of phonon excitations n.sub.ph (i.e.,
|1.sub.i|0.sub.j|n.sub.ph+1.sub.m and
|0.sub.i|1.sub.j|n.sub.ph-1.sub.m) can be removed by a sufficiently
complex pulse sequence, and thus the combined state of the two
qubits and the motional mode after the XX-gate operation may be
considered disentangled as the initial number of phonon excitations
n.sub.ph in the m-th motional mode stays unchanged at the end of
the XX-gate operation. Thus, qubit states before and after the
XX-gate operation will be described below generally without
including the motional modes.
More generally, the combined state of i-th and j-th qubits
transformed by the application of pulses on the sidebands for
duration .tau. (referred to as a "gate duration"), having
amplitudes .OMEGA..sup.(i) and .OMEGA..sup.(j) and detuning
frequency .mu., can be described in terms of an entangling
interaction .chi..sup.(i,j)(.tau.) as follows:
|0.sub.i|0.sub.j.fwdarw.cos(2.chi..sup.(i,j)(.tau.))|0.sub.i|0.sub.j-i
sin(2.chi..sup.(i,j))(.tau.))|1.sub.i|1.sub.j
|0.sub.i|1.sub.j.fwdarw.cos(2.chi..sup.(i,j)(.tau.))|0.sub.i|1.sub.j-i
sin(2.chi..sup.(i,j))(.tau.))|1.sub.i|0.sub.j
|1.sub.i|0.sub.j.fwdarw.-i
sin(2.chi..sup.(i,j))(.tau.))|0.sub.i|1.sub.nj+cos(2.chi..sup.(i,j)(.tau.-
))|1.sub.i|0.sub.j |1.sub.i|1.sub.j.fwdarw.-i
sin(2.chi..sup.(i,j))(.tau.))|0.sub.i|0.sub.j+cos(2.chi..sup.(i,j)(.tau.)-
)|1.sub.i|1.sub.j where,
.chi..function..tau..times..times..eta..times..eta..times..intg..tau..tim-
es..times..intg..times..times..OMEGA..function..times..OMEGA..function..ti-
mes..function..mu..times..times..function..mu..times..times..function..ome-
ga..function. ##EQU00001## and .eta..sub.m.sup.(i) is the
Lamb-Dicke parameter that quantifies the coupling strength between
the i-th ion and the m-th motional mode having the frequency
.omega..sub.m, and M is the number of the motional modes (equal to
the number N of ions in the group 106).
The entanglement interaction between two qubits described above can
be used to perform an XX-gate operation. The XX-gate operation (XX
gate) along with single-qubit operations (R gates) forms a set of
gates {R, XX} that can be used to build a quantum computer that is
configured to perform desired computational processes. Among
several known sets of logic gates by which any quantum algorithm
can be decomposed, a set of logic gates, commonly denoted as {R,
XX}, is native to a quantum computing system of trapped ions
described herein. Here, the R gate corresponds to manipulation of
individual qubit states of trapped ions, and the XX gate (also
referred to as an "entangling gate") corresponds to manipulation of
the entanglement of two trapped ions.
To perform an XX-gate operation between i-th and j-th qubits,
pulses that satisfy the condition
.chi..sup.(i,j)(.tau.)=.theta..sup.(i,j)(0<.theta..sup.(i,j).ltoreq..p-
i./8) (i.e., the entangling interaction .chi..sup.(i,j)(.tau.) has
a desired value .theta..sup.(i,j), referred to as condition for a
non-zero entanglement interaction) are constructed and applied to
the i-th and the j-th qubits. The transformations of the combined
state of the i-th and the j-th qubits described above corresponds
to the XX-gate operation with maximal entanglement when
.theta..sup.(i,j)=.pi./8. Amplitudes .OMEGA..sup.(i)(t) and
.OMEGA..sup.(j)(t) of the pulses to be applied to the i-th and the
j-th qubits are control parameters that can be adjusted to ensure a
non-zero tunable entanglement of the i-th and the j-th qubits to
perform a desired XX gate operation on i-th and j-th qubits.
Hybrid Quantum-Classical Computing System
While currently available quantum computers may be noisy and prone
to errors, a combination of both quantum and classical computers,
in which a quantum computer is a domain-specific accelerator, may
be able to solve optimization problems that are beyond the reach of
classical computers. An example of such optimization problems is
quantum chemistry, where Variational Quantum Eigensolver (VQE)
algorithms perform a search for the lowest energy (or an energy
closest to the lowest energy) of a many-particle quantum system,
such as a large molecules chemical compound and the corresponding
state (e.g. a configuration of the interacting electrons or spins)
by iterating computations between a quantum processor and a
classical computer. A many-particle quantum system in quantum
theory is described by a model Hamiltonian and the energy of the
many-particle quantum system corresponds to the expectation value
of the model Hamiltonian. In such algorithms, a configuration of
electrons or spins that is best known approximation calculated by
the classical computer is input to the quantum processor as a trial
state and the energy of the trial state is estimated using the
quantum processor. The classical computer receives this estimate,
modifies the trial state by a known classical optimization
algorithm, and returns the modified trial state back to the quantum
processor. This iteration is repeated until the estimate received
from the quantum processor is within a predetermined accuracy. A
trial function (i.e., a possible configuration of electrons or
spins of the many-particle quantum system) would require
exponentially large resource to represent on a classical computer,
as the number of electrons or spins of the many-particle quantum
system of interest, but only require linearly-increasing resource
on a quantum processor. Thus, the quantum processor acts as an
accelerator for the energy estimation sub-routine of the
computation. By solving for a configuration of electrons or spins
having the lowest energy under different configurations and
constraints, a range of molecular reactions can be explored as part
of the solution to this type of optimization problem for
example.
Another example optimization problem is in solving combinatorial
optimization problems, where Quantum Approximate Optimization
Algorithm (QAOA) perform search for optimal solutions from a set of
possible solutions according to some given criteria, using a
quantum computer and a classical computer. The combinatorial
optimization problems that can be solved by the methods described
herein may include the PageRank (PR) problem for ranking web pages
in search engine results and the maximum-cut (MaxCut) problem with
applications in clustering, network science, and statistical
physics. The MaxCut problem aims at grouping nodes of a graph into
two partitions by cutting across links between them in such a way
that a weighted sum of intersected edges is maximized. The
combinatorial optimization problems that can be solved by the
methods described herein may further include the travelling
salesman problem for finding shortest and/or cheapest round trips
visiting all given cities. The travelling salesman problem is
applied to scheduling a printing press for a periodical with
multi-editions, scheduling school buses minimizing the number of
routes and total distance while no bus is overloaded or exceeds a
maximum allowed policy, scheduling a crew of messengers to pick up
deposit from branch banks and return the deposit to a central bank,
determining an optimal path for each army planner to accomplish the
goals of the mission in minimum possible time, designing global
navigation satellite system (GNSS) surveying networks, and the
like. Another combinatorial optimization problem is the knapsack
problem to find a way to pack a knapsack to get the maximum total
value, given some items. The knapsack problem is applied to
resource allocation given financial constraints in home energy
management, network selection for mobile nodes, cognitive radio
networks, sensor selection in distributed multiple radar, or the
like.
A combinatorial optimization problem is modeled by an objective
function (also referred to as a cost function) that maps events or
values of one or more variables onto real numbers representing
"cost" associated with the events or values and seeks to minimize
the cost function. In some cases, the combinatorial optimization
problem may seek to maximize the objective function. The
combinatorial optimization problem is further mapped onto a simple
physical system described by a model Hamiltonian (corresponding to
the sum of kinetic energy and potential energy of all particles in
the system) and the problem seeks the low-lying energy state of the
physical system, as in the case of the Variational Quantum
Eigensolver (VQE) algorithm.
This hybrid quantum-classical computing system has at least the
following advantages. First, an initial guess is derived from a
classical computer, and thus the initial guess does not need to be
constructed in a quantum processor that may not be reliable due to
inherent and unwanted noise in the system. Second, a quantum
processor performs a small-sized (e.g., between a hundred qubits an
a few thousand qubits) but accelerated operation (that can be
performed using a small number of quantum logic gates) between an
input of a guess from the classical computer and a measurement of a
resulting state, and thus a NISQ device can execute the operation
without accumulating errors. Thus, the hybrid quantum-classical
computing system may allow challenging problems to be solved, such
as small but challenging combinatorial optimization problems, which
are not practically feasible on classical computers, or suggest
ways to speed up the computation with respect to the results that
would be achieved using the best known classical algorithm.
FIGS. 7 and 8 depict an overall hybrid quantum-classical computing
system 700 and a flowchart illustrating a method 800 of obtaining a
solution to an optimization problem by Variational Quantum
Eigensolver (VQE) algorithm or Quantum Approximate Optimization
Algorithm (QAOA) according to one embodiment. In this example, the
quantum processor is the group 106 of N trapped ions, in which the
two hyperfine states of each of the N trapped ions form a
qubit.
The VQE algorithm relies on a variational search by the well-known
Rayleigh-Ritz variational principle. This principle can be used
both for solving quantum chemistry problems by the VQE algorithm
and combinatorial optimization problems solved by the QAOA. The
variational method consists of iterations that include choosing a
"trial state" of the quantum processor depending on a set of one or
more parameters (referred to as "variational parameters") and
measuring an expectation value of the model Hamiltonian (e.g.,
energy) of the trial state. A set of variational parameters (and
thus a corresponding trial state) is adjusted and an optimal set of
variational parameters are found that minimizes the expectation
value of the model Hamiltonian (the energy). The resulting energy
is an approximation to the exact lowest energy state. As the
processes for obtaining a solution to an optimization problem by
the VQE algorithm and by the QAOA, the both processes are described
in parallel below
In block 802, by the classical computer 102, an optimization
problem to be solved by the VQE algorithm or the QAOA is selected,
for example, by use of a user interface of the classical computer
102, or retrieved from the memory of the classical computer 102,
and a model Hamiltonian H.sub.C, which describes a many-particle
quantum system in the quantum chemistry problem, or to which the
selected combinatorial optimization problem is mapped, is
computed.
In a quantum chemistry problem defined on an N-spin system, the
system can be well described by a model Hamiltonian that includes
quantum spins (each denoted by the third Pauli matrix
.sigma..sub.i.sup.z) (i=1, 2, . . . , N) and couplings among the
quantum spins .sigma..sub.i.sup.z,
H.sub.C=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.P.sub..alpha.,
where P.sub..alpha. is a Pauli string (also referred to as a Pauli
term)
P.sub..alpha.=.sigma..sub.1.sup..alpha..sup.1.sigma..sub.2.sup..alpha..su-
p.2 . . . .sigma..sub.N.sup..alpha..sup.N and
.sigma..sub.i.sup..alpha..sup.i is either the identity operator I
or the Pauli matrix .sigma..sub.i.sup.X, .sigma..sub.i.sup.Y, or
.sigma..sub.i.sup.Z. Here t stands for the number of couplings
among the quantum spins and h.sub..alpha. (.alpha.=1, 2, . . . , t)
stands for the strength of the coupling .alpha.. An N-electron
system can be also described by the same model Hamiltonian
H.sub.C=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.P.sub..alpha.. The
goal is to find low-lying energy states of the model Hamiltonian
H.sub.C.
In a combinatorial optimization problem defined on a set of N
binary variables with t constrains (.alpha.=1, 2, . . . t), the
objective function is the number of satisfied clauses
C(z)=.SIGMA..sub..alpha.=1.sup.tC.sub..alpha.(z) or a weighted sum
of satisfied clauses
C(z)=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.C.sub..alpha.(z)
(h.sub..alpha. corresponds to a weight for each constraint
.alpha.), where z=z.sub.1 z.sub.2 . . . z.sub.N is a N-bit string
and C.sub..alpha.(z)=1 if z satisfies the constraint .alpha.. The
clause C.sub..alpha.(z) that describes the constraint .alpha.
typically includes a small number of variables z.sub.i. The goal is
to minimize the objective function. Minimizing this objective
function can be converted to finding a low-lying energy state of a
model Hamiltonian
H.sub.C=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.P.sub..alpha. by
mapping each binary variable z.sub.i to a quantum spin
.sigma..sub.i.sup.z and the constraints to the couplings among the
quantum spins .sigma..sub.i.sup.z, where P.sub..alpha. is a Pauli
string (also referred to as a Pauli term)
P.sub..alpha.=.sigma..sub.1.sup..alpha..sup.1.sigma..sub.2.sup..alpha..su-
p.2 . . . .sigma..sub.N.sup..alpha..sup.N and
.sigma..sub.i.sup..alpha..sup.i is either the identity operator I
or the Pauli matrix .sigma..sub.i.sup.X, .sigma..sub.i.sup.Y, or
.sigma..sub.i.sup.Z. Here t stands for the number of couplings
among the quantum spins and h.sub..alpha. (.alpha.=1, 2, . . . , t)
stands for the strength of the coupling .alpha..
The quantum processor 106 has N qubits and each quantum spin
.sigma..sub.i.sup.z (i=1, 2, . . . , N) is encoded in qubit i (i=1,
2, . . . , N) in the quantum processor 106. For example, the
spin-up and spin-down states of the quantum spin
.sigma..sub.i.sup.z are encoded as |0 and |1 of the qubit i.
In block 804, following the mapping of the selected combinatorial
optimization problem onto a model Hamiltonian
H.sub.C=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.P.sub..alpha., a
set of variational parameters ({right arrow over
(.theta.)}=.theta..sub.1, .theta..sub.2, . . . , .theta..sub.N for
the VQE algorithm, ({right arrow over (.gamma.)}=.gamma..sub.1,
.gamma..sub.2, . . . , .gamma..sub.p, {right arrow over
(.beta.)}=.beta..sub.1, .beta..sub.2, . . . , .beta..sub.p) for the
QAOA) is selected, by the classical computer 102, to construct a
sequence of gates (also referred to a "trial state preparation
circuit") A({right arrow over (.theta.)}) for the VQE or A({right
arrow over (.gamma.)}, {right arrow over (.beta.)}) for the QAOA,
which prepares the quantum processor 106 in a trial state
|.PSI.({right arrow over (.theta.)}) for the VQE or |.PSI.({right
arrow over (.gamma.)}, {right arrow over (.beta.)}) for the QAOA.
For the initial iteration, a set of variational parameters {right
arrow over (.theta.)} in the VQE may be chosen randomly. In the
QAOA, a set of variational parameters ({right arrow over
(.gamma.)}, {right arrow over (.beta.)}) may be randomly chosen for
the initial iteration.
This trial state |.PSI.({right arrow over (.theta.)}),
|.PSI.({right arrow over (.gamma.)}, {right arrow over (.beta.)})
is used to provide an expectation value of the model Hamiltonian
H.sub.C.
In the VQE algorithm, the trial state preparation circuit A({right
arrow over (.theta.)}) may be constructed by known methods, such as
the unitary coupled cluster method, based on the model Hamiltonian
H.sub.C and the selected set of variational parameters {right arrow
over (.theta.)}.
In the QAOA, the trial state preparation circuit A({right arrow
over (.gamma.)}, {right arrow over (.beta.)}) includes p layers
(i.e., p-time repetitions) of an entangling circuit
U(.gamma..sub.l) that relates to the model Hamiltonian H.sub.C
(U(.gamma..sub.l)=e.sup.-i.gamma..sup.l.sup.H.sup.C) and a mixing
circuit U.sub.Mix(.beta..sub.l) that relates to a mixing term
H.sub.B=.SIGMA..sub.i=1.sup.n.sigma..sub.i.sup.X
(U.sub.Mix(.beta..sub.l)=e.sup.-i.beta..sup.l.sup.H.sup.B) (l=1, 2,
. . . , p) as A({right arrow over (.gamma.)},{right arrow over
(.beta.)})=U.sub.Mix(.beta..sub.p)U.sub.Mix(.beta..sub.p-1)U(.gamma..sub.-
p-1) . . . U.sub.Mix(.beta..sub.1)U(.gamma..sub.1). Each term
.sigma..sub.i.sup.X in the mixing term H.sub.B corresponds to a
.pi./2-pulse (as described above in relation to FIG. 4) applied to
qubit i in the quantum processor 106.
To allow the application of the trial state preparation circuit
A({right arrow over (.theta.)}), A({right arrow over (.gamma.)},
{right arrow over (.beta.)}) on a NISQ device, the number of the
quantum gate operations need to be small (i.e., shallow circuits)
such that errors due to the noise in the NISQ device are not
accumulated. However, as the problem size increases, the complexity
of the trial state preparation circuit A({right arrow over
(.theta.)}), A({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) may increase rapidly, leading to deep circuits (i.e., an
increased number of time steps required to execute gate operations
in circuits to construct) required to construct the trial state
preparation circuit A({right arrow over (.theta.)}), A({right arrow
over (.gamma.)}, {right arrow over (.beta.)}). Furthermore, some
trial state preparation circuit A({right arrow over (.theta.)}),
A({right arrow over (.gamma.)}, {right arrow over (.beta.)}) that
are designed hardware-efficiently with shallow circuits (i.e., a
decreased number of time steps required to execute gate operations)
may not provide a large enough variational search space to find the
lowest energy of the model Hamiltonian H.sub.C.
In the embodiments described herein, the terms in the model
Hamiltonian H.sub.C are grouped into sub-Hamiltonians H.sub..lamda.
(.lamda.=1, 2, . . . , u), where u is the number of
sub-Hamiltonians (i.e.,
H.sub.C=.SIGMA..sub..lamda.=1.sup.uH.sub..lamda.), and the trial
state preparation circuit A({right arrow over (.theta.)}), A({right
arrow over (.gamma.)}, {right arrow over (.beta.)}) is replaced
with a reduced state preparation circuit
A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) to evaluate an expectation value of each
sub-Hamiltonian H.sub..lamda.. The reduced state preparation
circuit A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) for a sub-Hamiltonian H.sub..lamda. is constructed
by a set of gate operations that can influence an expectation value
of the sub-Hamiltonian H.sub..lamda. (referred to as the past
causal cone (PCC) of the sub-Hamiltonian). Other gate operations
(that do not influence the expectation value of the sub-Hamiltonian
H.sub..lamda.) in the trial state preparation circuit A({right
arrow over (.theta.)}), A({right arrow over (.gamma.)}, {right
arrow over (.beta.)}) are removed in the reduced state preparation
circuits A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}). In some embodiments, sub-Hamiltonians
H.sub..lamda. of the model Hamiltonian H.sub.C may respectively
correspond to Pauli terms P.sub..alpha. in the model Hamiltonian
H.sub.C. In some embodiments, a sub-Hamiltonian H.sub..lamda. is a
collection of more than one Pauli terms P.sub..alpha. in the model
Hamiltonian H.sub.C.
For example, the model Hamiltonian
H.sub.C=.sigma..sub.1.sup.Z.sigma..sub.2.sup.Z+.sigma..sub.2.sup.Z.sigma.-
.sub.3.sup.Z+.sigma..sub.3.sup.Z.sigma..sub.4.sup.Z+.sigma..sub.1.sup.Z.si-
gma..sub.4.sup.Z defined on a system of four qubits (qubit 1, 2, .
. . , 4) may be grouped into four sub-Hamiltonians,
H.sub.1=.sigma..sub.1.sup.Z.sigma..sub.2.sup.Z,
H.sub.2=.sigma..sub.2.sup.Z.sigma..sub.3.sup.Z,
H.sub.3=.sigma..sub.3.sup.Z.sigma..sub.4.sup.Z, and
H.sub.4=.sigma..sub.1.sup.Z.sigma..sub.4.sup.Z. FIG. 9A illustrates
the trial state preparation circuit (A({right arrow over
(.gamma.)}, {right arrow over
(.beta.)})=U(.gamma..sub.1)U.sub.Mix(.beta..sub.1)) 900, where p=1.
The mixing circuit U.sub.Mix(.beta..sub.l) can be implemented by
single-qubit rotation gates 902, 904, 906, 908 on qubits 1, 2, 3,
and 4, respectively. The entangling circuit U(.gamma..sub.1) is
related to the model Hamiltonian H.sub.C as described above. The
first term .sigma..sub.1.sup.Z.sigma..sub.2.sup.Z in the model
Hamiltonian H.sub.C can be implemented in combination of
controlled-NOT gates 910 on qubit 2 conditioned on qubit 1 and
targeted on qubit 2, and a single-qubit rotation gate 912 on qubit
2 about the Z-axis of the Bloch sphere 400 by a polar angle
.gamma..sub.1/2. As one will appreciate, the implementation of such
gates can be performed by combining properly adjusted XX-gate
operation between qubits 1 and 2 and composite pulses applied to
qubits 1 and 2. Other terms in the model Hamiltonian H.sub.C can be
implemented similarly. FIG. 9B illustrates the reduced state
preparation circuits (A.sub.PCC.sup.1({right arrow over (.gamma.)},
{right arrow over (.beta.)})) 914 to evaluate an expectation value
of the sub-Hamiltonian
H.sub.1=.sigma..sub.1.sup.Z.sigma..sub.2.sup.Z. Since qubits 3 and
4 do not affect the expectation value of the sub-Hamiltonian
H.sub.1=.sigma..sub.1.sup.Z.sigma..sub.2.sup.Z, the set of gates
916 and the single-qubit rotation gates 906, 908 (illustrated in
FIG. 9A) that are applied only to qubits 3 and 4 in the trial state
preparation circuit A({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) are removed. FIG. 9C illustrates the reduced state
preparation circuits (A.sub.PCC.sup.2({right arrow over (.gamma.)},
{right arrow over (.beta.)})) 918 to evaluate an expectation value
of the sub-Hamiltonian
H.sub.2=.sigma..sub.2.sup.Z.sigma..sub.3.sup.Z. Since qubits 1 and
4 do not affect the expectation value of the sub-Hamiltonian
H.sub.2=.sigma..sub.2.sup.Z.sigma..sub.3.sup.Z, the set of gates
920 and the single-qubit rotation gates 902, 908 (illustrated in
FIG. 9A) that are applied only to qubits 1 and 4 in the trial state
preparation circuit A({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) are removed. The reduced state preparation circuits
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) to evaluate an expectation value of other
sub-Hamiltonians H.sub..lamda. can be constructed similarly.
With the reduced trial state preparation circuit
A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) for a sub-Hamiltonian H.sub..lamda., a trial state
|.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) is prepared on the quantum processor 106 to evaluate an
expectation of the sub-Hamiltonian H.sub..lamda.. This step is
repeated for all of the sub-Hamiltonians H.sub..lamda. (.lamda.=1,
2, . . . , u). The expectation value of the model Hamiltonian
H.sub.C is a sum of the expectation values of all of the
sub-Hamiltonians H.sub..lamda. (.lamda.=1, 2, . . . , u). The use
of the reduced trial state preparation circuit
A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) reduces the number of gate operations to apply on
the quantum processor 106. Thus, a trial state
|.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) can be constructed without accumulating errors due to
the noise in the NISQ device.
In block 806, following the selection of a set of variational
parameters {right arrow over (.theta.)}, ({right arrow over
(.gamma.)}, {right arrow over (.beta.)}), the quantum processor 106
is set in an initial state |.PSI..sub.0 by the system controller
104. In the VQE algorithm, the initial state |.PSI..sub.0 may
correspond to an approximate ground state of the system that is
calculated by a classical computer or an approximate ground state
that is empirically known to one in the art. In the QAOA algorithm,
the initial state |.PSI..sub.0 may be in the hyperfine ground state
of the quantum processor 106 (in which all qubits are in the
uniform superposition over computational basis states (in which all
qubits are in the superposition of |0 and |1, |0+|1). A qubit can
be set in the hyperfine ground state |0 by optical pumping and in
the superposition state |0+|1 by application of a proper
combination of single-qubit operations (denoted by "H" in FIG. 7)
to the hyperfine ground state |0.
In block 808, following the preparation of the quantum processor
106 in the initial state |.PSI..sub.0, the trial state preparation
circuit A({right arrow over (.theta.)}), A({right arrow over
(.gamma.)}, {right arrow over (.beta.)}) is applied to the quantum
processor 106, by the system controller 104, to construct the trial
state |.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) for evaluating an expectation of the sub-Hamiltonian
H.sub..lamda.. The reduced trial state preparation circuit
A.sub.PCC.sup..lamda.({right arrow over (.theta.)}),
A.sub.PCC.sup..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)}) is decomposed into series of XX-gate operations (XX
gates) and single-qubit operations (R gates) and optimized by the
classical computer 102. The series of XX-gate operations (XX gates)
and single-qubit operations (R gates) can be implemented by
application of a series of laser pulses, intensities, durations,
and detuning of which are appropriately adjusted by the classical
computer 102 on the set initial state |.PSI..sub.0 and transform
the quantum processor from the initial state |.PSI..sub.0 to trial
state |.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}).
In block 810, following the construction of the trial state
|.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)})on the quantum processor 106, the expectation value
F.sub..lamda.({right arrow over
(.theta.)})=.PSI..sub..lamda.({right arrow over
(.theta.)})|H.sub..lamda.|.PSI..sub..lamda.({right arrow over
(.theta.)})F.sub..lamda.({right arrow over (.gamma.)}, {right arrow
over (.beta.)})=.PSI..sub..lamda.({right arrow over (.gamma.)},
{right arrow over (.beta.)})|H.sub..lamda.|.PSI..sub..lamda.({right
arrow over (.gamma.)}, {right arrow over (.beta.)}) of the
sub-Hamiltonian H.sub..lamda.).lamda.=1, 2, . . . , u) is measured
by the system controller 104. Repeated measurements of populations
of the trapped ions in the group 106 of trapped ions the trial
state |.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) (by collecting fluorescence from each trapped ion and
mapping onto the PMT 110) yield the expectation value the
sub-Hamiltonian H.sub..lamda..
In block 812, following the measurement of the expectation value of
the sub-Hamiltonian H.sub..lamda. (.lamda.=1, 2, . . . , u), blocks
806 to 810 for another sub-Hamiltonian H.sub..lamda. (.lamda.=1, 2,
. . . , u) until the expectation values of all the sub-Hamiltonian
H.sub..lamda. (.lamda.=1, 2, . . . , u) in the model Hamiltonian
H.sub.C=.SIGMA..sub..lamda.=1.sup.uH.sub..lamda. have been measured
by the system controller 104.
In block 814, following the measurement of the expectation values
of all the sub-Hamiltonian H.sub..lamda. (.lamda.=1, 2, . . . , u),
a sum of the measured expectation values of all the sub-Hamiltonian
H.sub..lamda. (.lamda.=1, 2, . . . , u) of the model Hamiltonian
H.sub.C=.SIGMA..sub..lamda.=1.sup.uH.sub..lamda. (that is, the
measured expectation value of the model Hamiltonian H.sub.C,
F({right arrow over
(.theta.)})=.SIGMA..sub..lamda.=1.sup.uF.sub..lamda.({right arrow
over (.theta.)}), F({right arrow over (.gamma.)}, {right arrow over
(.beta.)})=.SIGMA..sub..lamda.=1.sup.uF.sub..lamda.({right arrow
over (.gamma.)}, {right arrow over (.beta.)}) is computed, by the
classical computer 102.
In block 816, following the computation of the measured expectation
value of the model Hamiltonian H.sub.C, the measured expectation
value F({right arrow over (.gamma.)}, {right arrow over (.beta.)})
of the model Hamiltonian H.sub.C is compared to the measured
expectation value of the model Hamiltonian H.sub.C in the previous
iteration, by the classical computer 102. If a difference between
the two values is less than a predetermined value (i.e., the
expectation value sufficiently converges towards a fixed value),
the method proceeds to block 820. If the difference between the two
values is more than the predetermined value, the method proceeds to
block 818.
In block 818, another set of variational parameters {right arrow
over (.theta.)}, ({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) for a next iteration of blocks 806 to 816 is computed by
the classical computer 102, in search for an optimal set of
variational parameters {right arrow over (.theta.)}, ({right arrow
over (.gamma.)}, {right arrow over (.beta.)}) to minimize the
expectation value of the model Hamiltonian H.sub.C, F({right arrow
over (.theta.)})=.SIGMA..sub..lamda.=1.sup.uF.sub..lamda.({right
arrow over (.theta.)}), F({right arrow over (.gamma.)}, {right
arrow over
(.beta.)})=.SIGMA..sub..lamda.=1.sup.uF.sub..lamda.({right arrow
over (.gamma.)}, {right arrow over (.beta.)}). That is, the
classical computer 102 will execute a classical optimization method
to find the optimal set of variational parameters {right arrow over
(.theta.)}, ({right arrow over (.gamma.)}, {right arrow over
(.beta.)})
.theta..times..times..function..theta..fwdarw..gamma..fwdarw..beta..fwdar-
w..times..times..function..gamma..fwdarw..beta..fwdarw.
##EQU00002## Example of conventional classical optimization methods
include simultaneous perturbation stochastic approximation (SPSA),
particle swarm optimization (PSO), Bayesian optimization (BO), and
Nelder-Mead (NM).
In block 820, the classical computer 102 will typically output the
results of the variational search to a user interface of the
classical computer 102 and/or save the results of the variational
search in the memory of the classical computer 102. The results of
the variational search will include the measured expectation value
of the model Hamiltonian H.sub.C in the final iteration
corresponding to the minimized energy of the system in the selected
quantum chemistry problem, or the minimized value of the objective
function C(z)=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.C.sub..alpha.
(z) of the selected combinatorial optimization problem (e.g., a
shortest distance for all of the trips visiting all given cities in
a travelling salesman problem) and the measurement of the trail
state |.PSI..sub..lamda.({right arrow over (.theta.)}),
|.PSI..sub..lamda.({right arrow over (.gamma.)}, {right arrow over
(.beta.)}) in the final iteration corresponding to the
configuration of electrons or spins that provides the lowest energy
of the system, or the solution to the N-bit string (z=z.sub.1
z.sub.2 . . . z.sub.N) that provides the minimized value of the
objective function
C(z)=.SIGMA..sub..alpha.=1.sup.th.sub..alpha.C.sub..alpha.(z) of
the selected combinatorial optimization problem (e.g., a route of
the trips to visit all of the given cities that provides the
shortest distance for a travelling salesman).
The variational search reduced trial state preparation circuits
described herein provides an improved method for obtaining a
solution to an optimization problem by the Variational Quantum
Eigensolver (VQE) algorithm or the Quantum Approximate Optimization
Algorithm (QAOA) on a hybrid quantum-classical computing system.
Thus, the feasibility that a hybrid quantum-classical computing
system may allow solving problems, which are not practically
feasible on classical computers, or suggest a considerable speed up
with respect to the best known classical algorithm even with a
noisy intermediate-scale quantum device (NISQ) device.
While the foregoing is directed to specific embodiments, other and
further embodiments may be devised without departing from the basic
scope thereof, and the scope thereof is determined by the claims
that follow.
* * * * *
References